Passive parameter based demographics generation

ABSTRACT

A method for characterizing website visitors based on visitor passive parameters and using the characterization to select and/or market website content. The passive visitor parameters include data in the browser agent, time of a website visit, IP address, etc. Such visitor passive parameters are available each time a visitor visits a website. In a first step, a first embodiment of the method anonymously compares the visitor passive parameters with known demographics, for example, at financial websites, to create a statistical mapping between the visitor passive parameters and the demographics. In a second step, the mapping is used to estimate demographics data for future website visitors and then site content provided to the future website visitors is based on the estimated demographics.

BACKGROUND OF THE INVENTION

The present invention relates in general to online Internet advertisingoptimization, and more specifically to analysis of relationships betweenweb browser agent data and characteristics and behavior of websitevisitors.

There are many advertising systems and methods which are used to selectadvertisements for display on Internet websites. These advertisingsystems use various strategies and logic to select which products and/orservices may be of interest to an individual website visitor and howadvertisements for the selected products should appear within a website.There are many competing ideas and many different approaches todesigning the logic which is used to select and display the advertising.

One such strategy is based on data which is collected for each websitevisitor. In this strategy, a unique identifier (commonly, an Internetbrowser cookie) is downloaded from the website to the visitors computer.This unique identifier allows the advertising system to tag the visitorand recognize the visitor's visits to the website as a discreteindividual. Further, data observed during the visit to the website iscollected and stored in a data base and indexed to the uniqueidentifier. This allows the advertising network to cross reference thestored data about the visitor in the database each time the visitorscomputer requests a web page. Observing a visitor's habits allows theadvertising network to better determine which ads to display based onthe stored data. A website may also retrieve visitor preferences andinterests stored at the website by identifying a returning visitor. Thedifferent kinds of data which may be gathered and the means ofreferencing the data based on the users identifier are important aspectsof the strategy.

One common way to gather visitor interests is to observe the visitor'spath through the website and noting the topics of the pages which thevisitor views. Another way to gather data is to request that the visitorfill out a survey and then store the survey information for future useby the advertising system when the visitor returns. A third common wayto gather this data is by saving information supplied by the visitorwhen purchasing goods. Oftentimes, the billing address given at theconclusion of an e-commerce transaction can be used to purchasedemographic data from companies which compile such information on a widebasis. Thus, there are several existing approaches to collecting andreferencing data for online advertising systems.

There are several concerns and problems with known methods of datacollection and indexing. One overarching issue is that the visitor'sprivacy is threatened by the combined data gathering. Another potentialissue is that the cookie used to store the visitor's unique identifierresides on the visitor's computer system. Visitors often delete thesecookies and thereby defeat the ability to recognize repeat visits. Inaddition, due to privacy concerns, a market has developed for softwareapplications which remove cookies placed by advertising systems. Theresult of removing the unique identifying cookie is that the advertisingnetwork can no longer reference information stored in the database forthat visitor, and may incorrectly identify future visits by the samevisitor as an additional visitor.

BRIEF SUMMARY OF THE INVENTION

The present invention addresses the above and other needs by providing amethod for characterizing website visitors based on visitor passiveparameters and using the characterization to select and/or marketwebsite content. The passive visitor parameters include data in thebrowser agent, time of a website visit, IP address, etc. Such visitorpassive parameters are available each time a visitor visits a website.In a first step, a first embodiment of the method anonymously comparesthe visitor passive parameters with known demographics, for example, atfinancial websites, to create a statistical mapping between the visitorpassive parameters and the demographics. In a second step, the mappingis used to estimate demographics data for future website visitors andthen site content provided to the future website visitors is based onthe estimated demographics.

The present invention further uses browser agent data to predict thecharacteristics of website visitors. First, data about thecharacteristics of website visitors (including, but not limited to,demographics information as well as specific behavioral information) ismatched statistically to user agent strings. Then, the user agentstrings are used to predict the characteristics of future visitors to awebsite. The use of the present user agent data for these purposesfacilitates the other unique aspects of the invention described in thefollowing.

The combination of the browser agent string and statistical analysis ofthe present invention has several unique benefits not present in knownmethods of predicting user characteristics or behavior. These benefitsare:

(1) The browser agent string is always present, unlike browser cookieswhich may be removed and cleared;

(2) Emerging internet platforms and devices (e.g. cellular phones, videogame consoles) do not have the capability to store browser cookies atall, while browser agents are still enabled and used;

(3) Similarly, certain applications which are becoming increasinglycommon, such as videos and Adobe Flash, do not support existing websitevisitor tracking technologies, but could be analyzed using methodsaccording to the present invention;

(4) The methods according to the present invention completely protectwebsite visitor privacy because individual website visitors are neveridentified during the statistical mapping phase, and the browser agentsdo not identify the website visitors; and

(5) Because the browser agent is stored in log files of existing webservers, the data may be post-analyzed without modifying the technicalinfrastructure of the website.

(6) The methods of applying statistical techniques to other websites'passive and active parameters provides superior insight incharacterizing web visitors not available with current methods orpassive parameters alone.

In accordance with one aspect of the invention, there is provided afirst method for characterizing web site visitors so that onlineadvertising can be adjusted and optimized. The first method comprisesobtaining a multiplicity of anonymous first website visitor passiveparameters from a first website for a multiplicity of first websitevisitors visiting the first website. Corresponding active parameters ofthe first website visitors are also obtained from the first website.Anonymous statistical mappings are generated between the first websitevisitor passive parameters and the corresponding first website visitoractive parameters. The statistical mappings are provided to websiteoperators, website publishers, search engine operators, and/or Internetadvertisers. The website operators, website publishers, and/or Internetadvertisers use the statistical mappings to improve the expected valueof future website content provided to the future website visitors, basedon future passive website visitor parameters of each of the futurewebsite visitors. Future website content is then provided to the futurewebsite visitors based on the expected value of the future websitecontent. The first passive website visitor parameters are commonlyavailable whenever a website is visited and do not identify the websitevisitor and the statistical mappings do not rely on any data identifyingan individual website visitor. The second website visitor parameters arecorrelatable to a value of website content presentable to the futurewebsite visitors.

In accordance with another aspect of the invention, there is provided amethod for marketing Internet advertising. The method for marketingInternet advertising includes obtaining a multiplicity of anonymousfirst website visitor passive parameters from a financial website for amultiplicity of financial website visitors visiting the financialwebsite. Corresponding financial website visitor demographics of thefinancial website visitors are obtained, where a value of websitecontent presentable to future website visitors is correlatable to thewebsite visitor demographics. Statistical mappings are anonymouslygenerated between the first passive website visitor parameters and thefinancial website visitor demographics of the financial websitevisitors, where the statistical mappings do not rely on any dataidentifying an individual website visitor. The statistical mappings aremarketed to website publishers, Internet advertisers, and/or secondwebsite operators. The statistical mappings are used to perform at leastone of identifying future website visitors having demographics desirableto the website publishers and Internet advertisers and demonstratingdemographics of the second website visitors visiting second websitesoperated by the second website operators.

In accordance with yet another aspect of the invention, there isprovided a second method for marketing Internet advertising. The secondmethod for marketing Internet advertising includes obtaining anonymousfirst website visitor passive parameters from an e-commerce website fora multiplicity of e-commerce website visitors visiting the e-commercewebsite, where the anonymous first passive website visitor parametersare commonly available whenever a website is visited. Transaction datacorresponding to the first passive website visitor passive parametersfor the e-commerce website visitors is also obtained from the e-commercewebsite. Statistical mappings are generated from the first websitevisitor passive parameters to the transaction data, where thestatistical mappings do not rely on any data identifying an individualwebsite visitor. The statistical mappings are marketed to websitepublishers and Internet advertisers and expected values of Internetadvertising to future website visitors based are estimated on futurewebsite visitor passive parameters of the future website visitors andthe statistical mappings. The Internet advertising is then allocated tothe future website visitors based on the expected values of theadvertising. The second method for marketing Internet advertising mayfurther include collecting additional transaction data for the Internetadvertising directed to the future website visitors and generatingimproved statistical mappings based on the additional transaction data.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

The above and other aspects, features and advantages of the presentinvention will be more apparent from the following more particulardescription thereof, presented in conjunction with the followingdrawings wherein:

FIG. 1 is a diagram of generation and application of a mapping betweenpassive website visitor parameters and demographics according to thepresent invention.

FIG. 2 is a diagram of a second embodiment of generation and applicationof a mapping between passive website visitor parameters and demographicsaccording to the present invention.

FIG. 3 describes a first method according to the present invention.

FIG. 4 describes a second method according to the present invention

FIG. 5 describes a third method according to the present invention.

FIG. 6 describes optional additional steps in the third method accordingto the present invention.

Corresponding reference characters indicate corresponding componentsthroughout the several views of the drawings.

DETAILED DESCRIPTION OF THE INVENTION

The following description is of the best mode presently contemplated forcarrying out the invention. This description is not to be taken in alimiting sense, but is made merely for the purpose of describing one ormore preferred embodiments of the invention. The scope of the inventionshould be determined with reference to the claims.

A general diagram of a method for improving targeting of Internetadvertising according to the present invention is shown in FIG. 1. Thediagram includes a first phase for generating mappings 20 betweenwebsite visitor passive parameters and demographics, and a second phaseof applying the mappings 20 to individual website visitor passiveparameters to obtain demographics and basing Internet advertising on thedemographics. In general, parameters available whenever a website isvisited are referred to herein as passive parameters and are describedin more detail below. The passive parameters are anonymous and do notidentify an individual website visitor. The passive visitor parametersmay include data in the browser agent, time of day when visitingwebsites, IP address, etc. for the website visitors 10 a. A browseragent is a set of parameters sent to a website by a website visitorvisiting the website to help the website determine how to format contentto send to the visitor. Such passive parameters are generally passivelyavailable when any visitor merely visits a website, that is, availablewithout requesting information from the visitor and without attemptingto interrogate the visitors computer.

Other parameters, referred to herein as active parameters, are availableat some websites, for example, demographics information at financialwebsites or transaction information available at e-commerce websites,are more personal, but may be obtained with corresponding passiveparameters without compromising a web site visitor's privacy. The activeparameters are generally directly related to behaviors or otherattributes which may be estimated using mappings from the passiveparameters.

In the first phase, passive parameters 12 a and correspondingdemographics 11 are received by a financial website 14 from firstwebsite visitors 10 a. The financial information includes the financialand other data used by the financial website 14 in its normal course ofoperation. Such information may include age, gender, education, address,income, etc. of each website visitor 10 a. The use of this data by thepresent invention does not require identifying individual websitevisitors, specifically, privacy is maintained. The combined passiveparameters and demographics data 16 is processed by a statisticalmapping generator 18 (described in more detail below) to generate thestatistical mappings 20 from the passive parameters to visitordemographics.

Continuing with FIG. 1, in the second phase, the statistical mappings 20are applied by a mapper 24 to individual second visitor passiveparameters 12 b (which do not include personal or financial information)received by an e-commerce website 22 from second website visitors 10 bto generate estimated demographics 26. The individual demographics 26and advertising demographic targets 30 a from advertisers 32 arecompared in advertisement selection 28 a to select Internet advertising34 provided to the website visitor 10 b. The selection of advertisingmay include both the objects advertised and the manner of presenting theadvertisements to the website visitor 10 b. The selection may also bethe result of bidding by the advertisers 32, where the bids are to someextent based on the estimated demographics 26. Further, the demographicsmappings 20 may be provided to the advertisers 32, and the advertisers32 may form advertising targeting strategy and negotiate with thee-commerce website 22 on advertising rates based on the visitorparameters 12 b. Several advertisers 32 may provide overlapping targets30 a, and the advertisement selection 28 a may select to provideadvertising 34 having the highest profit, increased user registration,or any result providing value to the advertiser or web site operator.

A diagram of a second embodiment of generation and application of amapping between passive website visitor parameters and active parametersaccording to the present invention is shown in FIG. 2. The secondembodiment provides the same passive parameters 12 a from first websitevisitors 10 a to an e-commerce website 22 a, but does not rely ondemographic information provided by the website visitors 10 a. Theactive parameters are transaction data from actual transactions on thee-commerce website 22 a. The combined passive parameters and transactiondata 16 is provided to the statistical mapping generator 16 to generatestatistical mappings 20.

In the second phase, the statistical mappings 20 are provided to theadvertisers 32, and the advertisers 32 provide second targets 30 b whichdescribe the passive visitor parameters 12 b the advertisers desire totarget advertising to. The e-commerce website 22 b then compares thepassive visitor parameters 12 b to the targets 30 b in a secondadvertisement selection 28 b to determine advertising 34 provided to thewebsite visitor 10 b. Several advertisers 32 may provide overlappingtargets 30 b, and the advertisement selection 28 b may select to provideadvertising 34 having the greatest value.

A method for selecting website content according to the presentinvention is described in FIG. 3. The method for selecting websitecontent includes obtaining a multiplicity of anonymous first websitevisitor passive parameters from a first website for a multiplicity offirst website visitors visiting the first website, where the anonymousfirst website visitor passive parameters are commonly available whenevera website is visited at step 100, obtaining corresponding first websitevisitor active parameters of the first website visitors from the firstwebsite, where the website visitor active parameters are correlatable toa value of website content presentable to future website visitors, atstep 102, generating anonymous statistical mappings between the firstwebsite visitor passive parameters of the first website visitors and thecorresponding first website visitor active parameters of the firstwebsite visitors, where the statistical mappings do not rely on any dataidentifying an individual website visitor, at step 104, marketing thestatistical mappings to one of website operators, website publishers,and/or Internet advertisers at step 106, using the statistical mappingsto establish an expected value of future website content provided to thefuture website visitors, based on future website visitor passiveparameters of each of the future website visitors, at step 108, andproviding future website content to the future website visitors based onthe expected value of the future website content at step 110.

A method for budgeting advertising to visitors based on the expectedvalues of the advertising according to the present invention isdescribed in FIG. 4. The method for budgeting advertising to visitorsbased on the expected values of the advertising includes obtaining amultiplicity of anonymous first website visitor passive parameters froma financial website for a multiplicity of financial website visitorsvisiting the financial website, where the anonymous first websitevisitor passive parameters are commonly available whenever a website isvisited, at step 200, obtaining corresponding financial website visitordemographics of the financial website visitors, where a value of websitecontent presentable to future website visitors is correlatable to thefinancial website visitor demographics, at step 202, anonymouslygenerating statistical mappings between the first website visitorpassive parameters and the financial website visitor demographics of thefinancial website visitors, where the statistical mappings do not relyon any data identifying an individual website visitor, at step 204,marketing the statistical mappings to at least one of websitepublishers, Internet advertisers, and/or second website operators atstep 206, using the statistical mappings to either identify futurewebsite visitors having demographics desirable to the website publishersand Internet advertisers at step 208, and/or demonstrating demographicsof the second website visitors visiting second websites by the secondwebsite operators to attract website publishers and/or Internetadvertisers at step 210.

A second method for marketing Internet advertising according to thepresent invention is described in FIG. 5. The method for marketingInternet advertising includes obtaining anonymous first website visitorpassive parameters from an e-commerce website for a multiplicity ofe-commerce website visitors visiting the e-commerce website, where theanonymous first website visitor passive parameters are commonlyavailable whenever a website is visited, at step 300, obtainingtransaction data from the e-commerce website corresponding to the firstwebsite visitor passive parameters for the e-commerce website visitorsat step 302, generating statistical mappings from the first websitevisitor passive parameters to the transaction data, where thestatistical mappings do not rely on any data identifying an individualwebsite visitor, at step 304, marketing the statistical mappings towebsite publishers and Internet advertisers at step 306, estimatingexpected values of Internet advertising to future website visitors basedon future website visitor passive parameters of the future websitevisitors and the statistical mappings at step 308, and allocating theInternet advertising to the future website visitors based on theexpected values of the advertising at step 310.

The method for marketing Internet advertising may further includeimproving the statistical mapping as shown in FIG. 6. The additionalsteps include collecting additional transaction data for the Internetadvertising directed to the future website visitors at step 312 andgenerating improved statistical mappings based on the additionaltransaction data at step 314.

As an example of the application of the present invention, an InternetSearch Engine (ISE) sends a large number of search engine visitors to ane-commerce website. The e-commerce website compares the value of the ISEvisitors to the e-commerce website to the visitor's passive parametersto obtain a statistical mapping. The e-commerce website shares thestatistical mapping with the ISE. The ISE then is more aggressive indisplaying the e-commerce website ads to ISE visitors of high value tothe e-commerce website and is less aggressive, or stops entirely,displaying ads to search engine visitors of low value to the e-commercewebsite. The ISE and the e-commerce website also adjust the pricing ofclicks and referrals based on the available parameters.

Several methods for generating the statistical mappings are available. Adirect mapping method uses a regression or other statistical techniquesto estimate the likelihood that the website visitor has a specificcharacteristic, based on their browser agent. First, a large database ofrecords which include website visitor active parameters (e.g., financialor personal information) from a website and corresponding websitevisitor passive parameter (e.g., browser agents) is collected, and aregression or other statistical techniques is used to estimate thelikelihood that the website visitor has a specific demographiccharacteristic, based on their browser agent. The financial and personalinformation and matching browser agent may be collected from, forexample, demographic information collected by a credit bureau. Somewebsites have usable logs which may be correlated to the financial andpersonal information. For example, virtually all web logs track browseragent and may also note the username. A record of the web browser agentand the specific website information (in the example, the consumerdemographic information, but not their name, Social security number, norother unique identifiers) is collected. When a large number of recordshave been collected, statistical techniques are employed, to match thebrowser agent to a demographic profile. For each user agent, a profile(for example, 60% male, 40% female; 20% over 50 years old, 30% are 30-49years old, 30% are 21-29 years old, 20% under 21 years old; 40% over$100 k income, etc.) is developed. Although a single visit at a websiteby a browser does not yield statistically useful information, asufficient number of visits creates a statistically significantdemographic profile.

Another method for generating statistical mappings is Multi-FactorMapping. In some instances, a website, might want to change its offeringto a visitor based on combinations of several factors. For example, aweb retailer may use web browser agents to define customer segments andtailor the advertising to the browser agent. As in the Direct Mappingexample above, statistically meaningful visitor information required togenerate mappings from the browser agent to visitor characteristics isobtained. A retailer may desire to split their visitors into foursegments: information hungry consumers that want lots of data beforethey buy; men that are active purchasers on the web; women that areactive purchasers on the web; and others. Visitor browser informationmay be collected from an advice site (for example, CNET) and definecertain browser agents which correlate with visitors seeking aninformation intensive web experience. For visitors not in theinformation intensive segment, information on demographics (e.g., from acredit bureau) and on tendency to purchase online (e.g., from e-commercewebsites) may be combined to identify browser agents which correlatewith gender, incomes, or other demographic data. Browser agents which donot correlate with any of the above groups may be correlated with, forexample, branding or other useful discriminates.

Further, the mappings may be continuously updated. Preferably, anadvertiser works with a search engine or publisher to increase the valueof their business relationship by continuously updating mappingsspecific to the advertiser/publisher combination. The advertiser maymonitor its own transactions and places a value on new transactions foreach browser agent. The advertiser may place clickable ads on thepublishers website. The publisher only displays the ads to consumerswith browser agents selected by the publisher. The advertiser may alsopay differing rates for clicks based on the browser agent. As visitorsclick-through to the advertisers site, the advertiser adjusts the valueof a single visitor with each browser agent. Depending on thecontractual relationship, the advertiser either passes this informationback to the publisher or simply adjusts the rates the advertiser offersto pay for traffic based on the browser agent and the source (i.e., thepublisher). Over time, the advertiser may assign different values for aparticular browser agent for each publisher. In other words, eachcombination of a browser agent and publisher will have an estimatedvalue (e.g., gross profit.)

There may be some browser agents which are very uncommon or are uncommonon a specific site. As a result, it may be difficult to assign, with anystatistical confidence, any characteristics (e.g., demographics) to thebrowser agent. As an alternative to the Direct Mapping described above,all of the data collected, including data from other websites, may beused to find a more common browser agent that has similarcharacteristics to the uncommon agent. For example, in order to estimatedemographic data for a website, rather than ignore an uncommon browseragent, use statistical techniques to assign characteristics to thebrowser agent. It may be determined that on other sites, a complicatedand unusual browser agent, such as: Mozilla/4.0 (compatible; MSIE 7.0;Windows NT 5.1; InfoPath.1)libwww-perl/5.808 has similar characteristicsto a simpler agent, such as: AMozilla/4.0 (compatible; MSIE 7.0; WindowsNT 5.1). The latter browser agents demographics may then be assigned tothe unusual browser agent.

In some instances, the browser agent will not be able to distinguishunusual results. For example, if a website has nearly 100% men (forexample, Slashdot), almost all browser agents will map into demographicprofiles which, using direct mapping, will show a more balancedpercentage of men. This problem may be addressed by doing a second-orderanalysis of the browser agents. An agreement may be made with a websiteassumed to have an unusual demographic profile (for example, almost allmen, women, young people, etc.). Their data may be collected andmappings generated using methods similar to the direct mapping example.A profile of which browser agents are present may be generated and whichare absent in websites with extreme profiles. Statistical techniques maythen be employed to match the distribution of browser agents to anextreme profile. In addition to the results of the direct mapping, theestimates may be adjusted based on the distribution of browser agents.

While the invention herein disclosed has been described by means ofspecific embodiments and applications thereof, numerous modificationsand variations could be made thereto by those skilled in the art withoutdeparting from the scope of the invention set forth in the claims.

1. A method for selecting website content, the method comprising:obtaining a multiplicity of anonymous first website visitor passiveparameters from a first website for a multiplicity of first websitevisitors visiting the first website, wherein the anonymous first websitevisitor passive parameters are commonly available whenever a website isvisited; obtaining corresponding first website visitor active parametersof the first website visitors from the first website, wherein the firstwebsite visitor active parameters are correlatable to a value of websitecontent presentable to future website visitors; generating anonymousstatistical mappings between the first website visitor passiveparameters of the first website visitors and the corresponding firstwebsite visitor active parameters of the first website visitors, whereinthe statistical mappings do not rely on any data identifying anindividual website visitor; providing the statistical mappings to atleast one of website operators, website publishers, search engineoperators and Internet advertisers; using the statistical mappings toestablish an expected value of future website content provided to thefuture website visitors, based on future website visitor passiveparameters of each of the future website visitors; and providing futurewebsite content to the future website visitors based on the expectedvalue of the future website content.
 2. The method of claim 1, whereinthe first website visitor passive parameters are selected from data in abrowser agent, time of day when visiting websites, and IP address forthe first website visitors.
 3. The method of claim 2, wherein the firstwebsite is a financial website and the first website visitor activeparameters are demographics parameters.
 4. The method of claim 2,wherein the first website is an e-commerce website and the first websitevisitor active parameters are transaction data.
 5. The method of claim1, wherein generating anonymous statistical mappings comprises usingdirect mapping including statistical techniques to estimate thelikelihood that the website visitor has a specific characteristic togenerate the anonymous statistical mappings.
 6. The method of claim 1,wherein generating anonymous statistical mappings comprises using directmapping including regression to estimate the likelihood that the websitevisitor has a specific characteristic to generate the anonymousstatistical mappings.
 7. The method of claim 1, wherein generatinganonymous statistical mappings comprises using Multi-Factor Mapping toestimate the likelihood that the website visitor has a specificcharacteristic to generate the anonymous statistical mappings.
 8. Amethod for marketing Internet advertising, the method comprising:obtaining a multiplicity of anonymous first website visitor passiveparameters from a financial website for a multiplicity of financialwebsite visitors visiting the financial website, wherein the anonymousfirst website visitor passive parameters are commonly available whenevera website is visited; obtaining corresponding financial website visitordemographics of the financial website visitors, wherein a value ofwebsite content presentable to future website visitors is correlatableto the financial website visitor demographics; anonymously generatingstatistical mappings between the first website visitor passiveparameters and the financial website visitor demographics of thefinancial website visitors, wherein the statistical mappings do not relyon any data identifying an individual website visitor; marketing thestatistical mappings to at least one of website publishers, Internetadvertisers, and second website operators; and using the statisticalmappings to perform at least one of: identifying future website visitorshaving demographics desirable to the website publishers and Internetadvertisers; and demonstrating demographics of the second websitevisitors visiting second websites operated by the second websiteoperators.
 9. A method for marketing Internet advertising, the methodcomprising: obtaining anonymous first website visitor passive parametersfrom a e-commerce website for a multiplicity of e-commerce websitevisitors visiting the e-commerce website, wherein the anonymous firstwebsite visitor passive parameters are commonly available whenever awebsite is visited; obtaining transaction data from the e-commercewebsite corresponding to the first website visitor passive parametersfor the e-commerce website visitors; generating statistical mappingsfrom the first website visitor passive parameters to the transactiondata, wherein the statistical mappings do not rely on any dataidentifying an individual website visitor; marketing the statisticalmappings to website publishers and Internet advertisers; estimatingexpected values of Internet advertising to future website visitors basedon future website visitor passive parameters of the future websitevisitors and the statistical mappings; and allocating the Internetadvertising to the future website visitors based on the expected valuesof the advertising.
 10. The method for marketing Internet advertising ofclaim 9, further comprising: collecting additional transaction data forthe Internet advertising directed to the future website visitors; andgenerating improved statistical mappings based on the additionaltransaction data.